{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff0d10c6-5f7f-4387-90f5-17ca872df414",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import random\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False#允许中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "36fd9699-43ff-449b-8a51-aa81ec0bdb4b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#读取数据\n",
    "df = pd.read_excel(\"C:\\workspace\\小组25.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6235aa59-4c06-4b51-b711-ab7b72c73c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['年份']\n",
    "y1=df['总计']\n",
    "y2=df['就业率']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c1155e9-6558-42db-a887-4456ca4d68f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HWbh3fApHHxHFcwtzWL2+lEmPZ/LwX1o12SjsIs2pLh0YwN9ctzvHy+Y/XWze6WLTn242/+mqUhtnNfw14oH+KAzXH4/BTkPBtDwKdWEkkKYQ04QUR/2aPkKRYRgMHxJHr852pryyl11ZHrLyvPRu7oKJNLLaOjDsG+dxzW+lfPuLk807/TVxhSUHH8i8Q2oE3TvaOeIwG0cf4a8pU6/U4KbQ3fIo1IUR/SqrXveOdl68py2r/uusVEOx/xh9IuEikA4MM9/OZXC/aFavd/L2Z4Xl660W6NLeRvfD7BzR0U73jjYO72A/6Hyq+2Z9Ua/U4KTQfXBb0108MS+HXVluhg2OY9Iljhr/DpimyYJPCvnPqiIKS3ycfmwM113iCMrZNRTqwoh+ldUsNtpSqbt+Vp6HR17ayy2XJ9OjU9Up0ERCVSC19lm5XtZtLmNgnyjcXpMjDrPTvaOdLu1s2G2BhzD1Sg1eCt1Vudwm98/KYmDvaB74SwrPL8zlw9XFnLff8DkH+uDrYt75opAp17UiNtrCP+ZkMyMth/vGt2rCkgdGoS6M6FdZ3by8OI8N21w881YOz9/pH6OvIR4qF2luu7M9AW2Xne9l6MBYBvY5tNlj1Cs1eCl0V/bdL06KnSY3jHQQZbcw8SIHzyzIrTHUffxtMaPPjKd3F//fznHDE3n01eqDcnNSqAsj+lVWNzeNTgZg3PmJGIZR54fKRYJNkdPH+ysKeeuTgoC2b8hae/VKDV4tJXSXlPoodlY8E2qLMKrUOm/Z5aZ3VztRdn/TabcONrZn1DzsS0GRl9bJFXHJavEPmB2MFOrCjH6VBS4+xsJ9E/zV54E+VC4SjHIKvPz7s0Le/7KQ4lL/MBYWA3w1jGihWvuWpSWE7glTMystjx2WUGkwZvAHv3YpFdHHMAwsFigs8RFfzXSbhx9mZ9XPTk7u7/8b8OE3xRzXOzjmRj9Q8Ia6ogJ49Ca4Yzq0autft2sbzHkK9mTAyefAyIn+MTpqe6+FaSm/yhpKXR4q1zWUYJKx18PC5QUs+6a4fOqtzu1sXHF2AvYI+Pt+Y1MeSLX2Em7mPNiWVo6KWGOLqPr9tlqqrrdHGJS6qg91Ey9ycM/ze7jlqd0Ul/r4Y5ebf97WumEL30CCM9QV5sNzD8Pe3RXr3C7/ur7HwnX3QtosWPUJDDm75vdaqJbwq6yh1OWhcl1TCQZb012kfVTAZz+W4Ptfa1PvLnbGnJPASUdFl8+cYrEYqrWXFiMmynLQXtr7i4+1sjXdVWldSZkPm7X6HzhtUyKY81A7dmR6+NfiXJLirUE79WRwhrqX/gHHnwZ//Faxbv0PUFIMl10HkVEwYgK8+bw/uNX0nkgtNBSMhIqMvR6eX5TLN+uc5euO6x3FmHMS6HdEZJVhGVRrL1JZz852PlhVMUh/ZrYHtwfiY2sOg4ZhEBNtsOb3Mp69vU1jF7PegjPUjb0FUtvBWy9WrPvzD+jW2x/aAA7rCuk7an+vOm4XePZ7OLLUWf22EtYCfVj80++L6d8jqsUOCSPNLzrSYM1vpRgGnNw/mjHnJNY6HI9q7UUq9OseSbHTx8erizj7xDjSPirgmJ7+HzrFTh+RdoOIamrt3lhWwKkDooN6CKzgDHWp7aquc5ZA6n7p2DDAYoXiwprfi40/+DE+WABL3mzYcktICmQoGIDV60u5+pF0Lhsaz+gzE4iOCr6BJyW01DSEjtdnsnJtCT9vKuOWy/09tR3xVu64yj+uYsc2tuYsukhIsloNbhuTzLQ52fxrcR5eH8y41f983MRpGUwemcSQ/lUfTdi1x81n3xcz+4GD5JMgEpyh7mCsVuCAm5jNBq6ymt+rLtQNGw1nj6hYzs2Gh65ryBJLiAhkKJi/XJjIN+uc/LrVxbwPClj6VRHjhzs4f3D4z6ErjaO2IXRy8r089lo2Hi+cdUIsff43X/HQgbHVfaSIBGBI/xjmTbGzcbuLvt0iccT7W1/SHu1Q7T4dWttY8nTHpipivYVOVUNsvL8Dxf5KnRARUfN71bHZITq24hUVnN2TpWnsGwom1VG5aTU1ycoj17biynMTee6ONjw8sRXtUyPIKfDx5doSBTqpl31D6BxYO7xvCJ0v15aQmhTBJafFM+78RDqkhs7vb5FQkOqIYHC/mPJAFy5C507RpQes/LBieW+m/5m42Pia3xMJUG0PlRuGwanHxDDo6GiWrCyi3xEVY3zlFXrJzPbQq4vG/ZKa1WUInRsuTWqiUolIOAidmroeR4GzGL5e7l9ethB6D/A/O1fTeyJ1sO+h8qEDY+nfI+qgvQRtEQYjTo/n8MMqHpad90E+f31iN68tzWvC0kooqssQOiIidRE6NXVWq79X7MvTYdErYPrgzidqf0+kkZmmicttYhjQ7wj1MpSaaQgdEWkswR3qXvmw8vKAQTBtNmzbBN17Q7wjsPdEGpFhGNxxVQpXnJ1Ah9YVHXbe/qwAnw8uPjW+yvyD0nIFOiSOhs4RkboK7lB3MEmt/K+6vifSyPYPdDkFXl5dkk9pmcm7Kwr5y4UOTsfAgn8qJy8G64wjyDYSSTHzOcrchJUaJuoMcjUNzSGVHdU9kpREa401cZqXVUTqI/RCnUgISIyzcOPIJOYszScz28u0Odm8HXEP13v/TT6xzIwYTZaRXL59qpnDZM8CTjHXNmOp66e2oTmkMqvFoHcXO1/9XP2A55qXVUTqI3Q6SoiEEKvFYNjgOOY90o4JFyQSHWnwu6Urt9ru4JGI68micq/GLJJ4JOJ6vjQGNGg5vD6TnzaW8un3xfy0sRSvr2FrAwMZmkMqy8z28O0v/kCXcMDURPuG0FEYFpH6UE2dSCOKjrRw9XmJnD84jrl3vskSy6n+GU8OZBhgmsyMGM1gn9kgtTSNXYNWl6E5VOtU4dX383B7oP8RkTxxcyrrt7jUbC0iDUKhTqQJJCdYOd33A0usp1W/kWGQRTLrNpcRE2Xh8x+K6d7RXmkGgb15HuJjLETaa65k31eDdqB9NWi11Qb5fCaFJT7yi3zkF3nJK6r4d36Rj7wiL/17RAY8NIfmHvX7bVsZy7/3115ef2kSEVaLro2INBiFOpEmkm0kBrZdvpedezwsWF7IiUdGVQp1Yx/JoNRlEh1p4Ii34oiz4Ii3khRvITHOiiPeQkKMhRcX59V4jGfeyimvQXO5TR57bS95hT7yi33kF3opKPZRW0ttYmxgvTM1NEeFeR/4Z7456/iYoJ4UXERCk0KdSBNJMfNr3wj/UBYRVoORZ8TTqW1Fj9oyl6/8mThnmYmzzENG9dPV1ii30Fdeg2aLgK//68RzkOwVG234w2KchYRYf4BMjLWQGG8lKSGwR3I1NEeFu65O4fVl+Yw+M6G5iyIiYUihTqSJHGVuItXM8XeSONhzdaZJKrkc1b0jVovBkYdXHtIi0m7hw2c6Ulxqkl/oJbfQR16hv2k0r9BLXqGX3CIfW3e52JbhqbU8+2rQDMPg1iuSibIbJP4vtDnirSTEWrBFVP98l9dn8up7+TU2wWpojsoc8VZuuiy59g1FROpBoU6kiVgxmexZwCMR14NpVg52pr8GbrJnAVZL/2o/wzAM4qIN4qItdGh98G1+2ljKbf/cU2t59q9BO29QXEDnsD+rxWDyqKSDPru3j4bm8MvK85Dq0O1WRBqX7jIiTegUcy2PeF70j1PHfuPUkdtg49Qd1T2SVIe1SWrQThkQwyPXtqrSy1YqFJX4uHZaJj0727l3XAqO+CZojp54buDbHjhzj4iELIU6kSZ2irmWwe6fGm1GiaauQTtlQAyD+0VXmlHi21+cLPikkKfn59C7q71F11Kt21JGSamPPble4mM0NKiINJ6We6cVaUZWTPqbG2msmcGqq0FLTbIyeWTDz/RgtRiVhubo2y2Sn34v4/cdLqbPzeaJm1pjaaHNsCcdFc2ch9pRVOLDam2Z10BEmoZCnUiYOlgNWlMNbmuLMLhvQgqT/pHJmt/LWPRpIaPPark9Pjuk2mrfSETkEKktQCSM7atBGzowlv49opq000LHNjYmj/JPhzb7/Tw2/elqsmMHgy07XWzc0bLOWUSal0KdiDSaYYNiGdIvGo8Xpr26l1KXr7mL1CRM02RGWg7XP57JB6uKmrs4ItJCKNSJSKMxDIPbr0wmJdHKjt0eXvx3XnMXqUmsWOvk160uouwGx/fVNGAi0jQU6kSkUSXGWblnXAoA768sYtV/S5q5RI3L5TZ5eXEuAJedGU+rFtzzV0SalkKdiDS6Y3tFcdmZ8QA8+UZOWM8H+96XhWRk+zumtOTOISLS9BTqRKRJXHOBg+6H2cgv8jH7/bzmLk6jyC/y8voH/jl+JwxPJDpSt1gRaTpqFxCRJmG3Gdw/oRULlhfw15FJzV2cRvH6sgKKnCbdOtg456TY5i5O+Ah0hgzNjiEtnEKdiDSZzu1s3HV1SnMXo1Hs3OPmvRWFAFw/wqE5b0WC1NZ0F0/My2FXlpthg+OYdIkDw6j5v9e3Pilg0fICytwmx/aK4rYxySTGNcGUf3WktgERaRamabL8u2LKwmSYk5ffzcPrg+P7RHFc7+jmLo6IHITLbXL/rCx6dLIz6562bM9w8+Hq4hr3+XlTKR+vLmbGbW341z1tcblNXnwnr2kKXEcKdSLSLJ6an8Njr2Xz0rt5zV2UQ/bfzaWs/MmJxYBJIxzNXRwRqcZ3vzgpdprcMNJBh1QbEy9ysOzrmkPdb9tcnNA3ik5tbHRobeOM42L5c7e7iUpcNwp1ItIsTu4fgy0C2qaE9lMgPp/JrP+NvzdscBxd29ubt0AiLVRJqY9iZ8XL5a46ufaWXW56d7UTZffHn24dbGzPqDmgdW1vY+XPTnZluckt9LLs6yKO6x2c40+G9t1URELWCX2jmT+1AymJwfdcSl18/mMJv293ER1pMP78xOYujkiLNWFqZqXlscMSGD/cUWldSamPdvv9kDQMA4sFCkt8xMccvJ7r+L7RHJZayNUPZwDQs7OdK84OzuGKFOpEpNnsH+hKXT4ibUatDywHm67tbRzbK4qjj4gkOcQDqkgom/Ng20qDfdsiqt5LrJaq6+0RBqWu6kPdFz8WszvHw2sPt8MRZ+HFd/J47LVsplyX2rAn0AAU6kSk2f2+vYxpc7IZcXo8F58a3/QFCHTIDKgybEa3DnaeuCkVX9WWHhFpQjFRFmKja36qLD7WytZ0V6V1JWU+bNbqf0x++kMJF5wcT6c2NgAmj0zigtt3UlTiI66aINhcgqs0ItIi/fKHi517PLz4Tl6VG26wMs2KFGcYhoYwEQkBPTvb2bC14h6Tme3B7YH42OrjkNdrkltYMQtOdoH/394g/CWnUCcize6S0+I4vk8ULrfJtDnZB33AOdjM+ncezy3MIb8ofKc8Ewk3/bpHUuz08fHqIgDSPirgmJ6RWC0GxU4fHm/Ve8+Rh0fyn6+KeP/LQj5aXcSjs/fSp6td49SJiByMYRjcdXUKiXEW/tjlDvppxLJyPSz+opDFXxTxx67gHNpARKqyWg1uG5PMjLRcLr17JyvWlnDtxQ4AJk7LYPU6Z5V9Rp6RwOnHxfD6sgKenp9DbLSFe8cH5yDqeqZORIJCcqKVO69K5oEX97Lo00IGBvEgvqlJEUy/sTXf/+pkQM/gHNpARA5uSP8Y5k2xs3G7i77dInHE+2vc0h7tcNDt7TaDmy5L5qbLmrKU9aOaOhEJGoOOjuHCk+MAmD4vuJs2j+kVxaQR4TmHrUi4S3VEMLhfTHmgCxcKdSISVK6/1EGnNhFk53t58o2cSh0SmpsPo9ID0yIiwUShTkSCSpTdwv3XtCLCCqv+6+Q/q2qewqcpLbecwFUPpbPo04LmLoqISBUKdSISdI7oaOcvFzoAmLkolx2Zzd8ZoRQ7s60X4ywzcXuCp/ZQRGQfhToRCUqjhsZzTM9Iytwm0+bsbfYg9W/LULKMZFonW7n09GYYIFlEpBYKdSISlCwWg7vHpZAQa2HTn26WflWE12fy08ZSPv2+mJ82ljbZ4J85xDPfeh4AEy90EGnXrVNEgo+GNBGRoJXqiOD2K5P5Y5ebpHgLYx5IJyvPu9/7ViaPSuKUATGNWo651gtxGlH09G3jjOM6NuqxRETqSz83RSSondw/hq7tbfx9dnalQAeQleflkZf38uXakkY7/lajHf+xnAzADd6FWDQdmIgEKYU6EQlqXp/JzEW5NW4z8+3cRmuKfck6Ep9hYYhvDUebmxvlGCIiDUGhTkSC2rrNZVVq6A6Uletl3eayBjumF4OfjB68YrmYby1HYTG9XOt5p8E+X0SkMeiZOhEJatn5gQ32e+/MPXRItdEmJYI2yVbapkTQJrni34lxFgyj9qbTL40BzIwYTZaRXL4uEjdbjQ50NPfU+zxERBqbQp2IBLWUxMCm8Slzwx/pbv5IP/iYdpE2ozzgHds7ilFDE8rfyyaRJAr4yujPIxHXV9nXSSSPRFzPI54XOaV+pyEi0ugU6kQkqB3VPZJUh7XGJthWDivTJ6eSledld46HzOx9/+thd46XnAIvZW6THbs97NjtqTTfo9tjcpltOhZ8OCj0rzywRs8wwDSZGTGawT4TqzpLiEgQUqgTkaBmtRhMHpXEIy/vrXabG0cl0bWDna4dDv6+y22SlesPeLtzPLRJqbj1Zed7MTCxYJJtJFVfEMMgi2TWbS6jf4+o+p6OiEijUUcJEQl6pwyI4ZFrW5HqqNwUm5pk5ZFrW9U6Tp3dZtChtY1jekVx3qA4julZEcrapkTwkXsyN3gXBVSWQJ/xExFpaqqpE5GQcMqAGAb3i2bd5jKy872kJFo5qntkgzSFWvHRxUwPaNtAn/ETEWlqCnUiEjKsFqPRmj6PMjeRauaQRVLVZ+oATJNUcjmqu2aUEJHgpOZXERHAislkzwL/gnnAQMb/W57sWaBOEiIStBTqRET+5xRzLY94XiSVyjNYpJLrH87EXNtMJRMRqZ2aX0VE9nOKuZbB7p9YZxxBtpFIipnPUeYmrDTONGQiIg1FoU5E5ABWTPqbG1GOE5FQouZXERERkTCgUCciIiISBkKv+fWb5bB4LhQXQrdeMO5v0Kot7NoGc56CPRlw8jkwcuLBhyUQERERCUOhFer2pPsD3eSHIC4RlrwBrz4Ft06D5x6GvsfCdfdC2ixY9QkMObu5Sywih2riuYFt98qHjVsOEZEgF1rNrzu2+GvnOh8BKa1h8Nmwexes/wFKiuGy66B1exgxAb7SDV5ERERajtCqqWvfCX77GXZshlbt4PMl0OcY+PMP6NYbIv830vxhXSF9R82f5XaBx12xXOpsvHKLiIhIUNia7uKJeTnsynIzbHAcky5xYNTwuNZrS/OY90FBlfVP/611o81wU18hFuo6w7FD4O83+pdbtYX7/wkfLITUNhXbGQZYrP7n7mLjD/5ZHyyAJW82epFFREQkOLjcJvfPymJg72ge+EsKzy/M5cPVxZx3Uly1+4w5J5GRZySUL+/O8XDns3vo3tHeFEWuk9AKdVs2wM/fwn3P+GvtPlgAzzwIvfoDtsrb2mzgKqs+1A0bDWePqFjOzYaHrmuskouIiEgz++4XJ8VOkxtGOoiyW5h4kYNnFuTWGOrsNgO7raIm71+LCxl5Rjxx0cH3BFvwlagm338JA0+Fbj0hKhouGQdZmf7gVphfedtSJ0TUkFltdoiOrXhFRTdu2UVERKTRlJT6KHZWvFzuqqOHb9nlpndXO1F2f/zp1sHG9gx3le2qszfPw1c/O7nktGoqjJpZaNXU+bz+JtV9SkugrNTf1PrHbxXr92b6n5errpZOREREwsqEqZmVlscOS2D8cEeldSWlPtqlVEQfwzCwWKCwxEd8TO31XEtWFjH0uBiio4KzTiy0Ql33PvDaDPj4HUhMgpUfQkISDL0IPlwIXy+HQWfCsoXQe4A/7ImIiEjYm/NgW1o5KmKNLaJq5werpep6e4RBqav2UOf1mfxnVRFP3dKmxu2aU2iFuoGnQuZOWP4u5OdAh87w1wf9zaxjb4GXp8OiV8D0wZ1PNHdpRUREpInERFmIreU5t/hYK1vTXZXWlZT5sFlrn6zgp41lJMZZ6dzOVuu2zSW0Qp1hwIVX+V8HGjAIps2GbZuge2+IdzR58UREahToQMqgwZRFGkHPznY+WFVUvpyZ7cHtgfjY2ptTv/ixmCH9gvv5++BsFK6vpFYw4CQFOhEREamiX/dIip0+Pl7tD3ZpHxVwTM9IrBaDYqcPj7dq54p9vv+1NOjGpTtQeIU6ERERkWpYrQa3jUlmRloul969kxVrS7j2YgcAE6dlsHrdwSci2JXlZm++l16dg29suv2FVvOriIiIyCEY0j+GeVPsbNzuom+3SBzx/k6VaY92qHafDqk2lj/fqamKWG8KdeFEz+uIiIjUKtURQaoj/CKQml9FREREwoBCnYiIiEgYUKgTERERCQMKdSIiIiJhQKFOREREJAwo1ImIiIiEAYU6ERERkTCgUCciIiISBsJv5D0JH4EOpqyBlEWahwY8FwkqqqkTERERCQMKdSIiIiJhQKFOREREJAwo1ImIiIiEAYU6ERERkTCgUCciIiISBjSkiUi40VAwIvWj/3YkxKmmTkRERCQMKNSJiIiIhAGFOhEREZEwoFAnIiIiEgYU6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAgw+LiIhIi7E13cUT83LYleVm2OA4Jl3iwDCMgPadOnsviXEWbh6d3MilrB/V1ImIiEiL4HKb3D8rix6d7My6py3bM9x8uLo4oH2//9XJ2t9LueYCR+MW8hAo1ImIiEiL8N0vToqdJjeMdNAh1cbEixws+7r2UFfm8vHPt3K59mIHcTHBG52Ct2QiIiIiASop9VHsrHi53GaVbbbsctO7q50ouz/+dOtgY3uGu9bPfn1ZAS63idUCa34vxTSrfnYw0DN1IlI/mvxcRILIhKmZlZbHDktg/HBHpXUlpT7apVREH8MwsFigsMRHfDU1cLtzPCz6tIBeXSLZnevl358X0jo5gr9f1yrgZ/GaikKdiIiIhLw5D7allaMi1tgiqgYuq6XqenuEQamr+lD30epikhKs/N9NrbHbDC49PZ4rHkjnhw2lDOwT3bAncYgU6kRERCTkxURZiI2u+amy+FgrW9NdldaVlPmwWauvccvK9XBMzyjsNqP8OB1aR5CZ7Tn0QjcwPVMnIiIiLULPznY2bK0IdZnZHtweiI+tPg61ToqgbL/n83w+k6xcL22Sg69eTKFOREREWoR+3SMpdvr4eHURAGkfFXBMz0isFoNipw+Pt2oHiFOPjeGbdU6+XFtCVq6HV97Lw+0xOfLwyAYv33vvvccVV1xR7/3rFepKS0tJT0/H6/XWab+srCycTmd9DikiIiJySKxWg9vGJDMjLZdL797JirUlXHuxA4CJ0zJYva5qRunUxsZD17TijWX5XP1IBqvXl/Lo9anERNUcoYqLi7nmmmv48ccfAdi1a1eVbT766CPS0tLKl3fs2MGyZcvqfX71qjtcsmQJl19+Ob///jvdu3cPeL+pU6fy22+/sXDhQhwOR30OLSIiIlJvQ/rHMG+KnY3bXfTtFokj3gpA2qMdqt3nxKOiOfGouneKeO2117jqqqv4+uuvueCCC3j99dcZNmwY4K8gmzRpErm5uZx//vkkJCQQGRmJ3W6v34lRz1AXGxuLaZo8/PDDdOzYkQ4dOtCtWzd69erF4YcfftB9vvrqK2bNmkW7du2CdnwXERERCX+pjghSHY37TFxUVBQASUlJbNiwAafTybhx41i1ahU9evTg//7v/9ixYwfz5s0jISEBAKvVitVqrfcx63VG+w749ttv43ZXDNpnGAYpKSkMGjSIM888k6uvvprExETWrl3LBRdcgGEYvP766yQlJdW7wCIiIiLBbl9WioqKYsyYMXTp0oXzzjuPc845hxUrVvD0008zYcIErrrqqkr7HcrYd/WOqYZhsHXrVqKiosjMzCQjI4MtW7bw7bffsmLFCt5//33uvfdeRo8ezfvvv09xcTFz587l1FNPrXdhRRqFBtEVEZFGsi/cDRo0iPfee48FCxbQqVMn0tLSOOGEE3jggQe49957iY2NPeRjBRTqPB4PLpeLmJiYKu8lJyeTnJxMnz59GDp0KNdddx0A77zzDmPHjmXOnDkAzJ0795B6dIiIiIiEsqOPPprTTjsNgIEDB3LJJZewcuVKCgsLeeaZZw758wPq/frJJ5/Qpk0bxowZw5tvvsnu3bsPup3T6eTDDz9k/PjxjB07FqfTyVlnnUVsbCyPPvoo2dnZh1xgERERkVAzbdo0xo4dC0BZWRknnHACX375JRdeeCH/+Mc/GuQYAdXUbdq0icTERN566y0WLFhQvn7GjBl0796ddevWsXbtWn788UdcLhcOh4OrrrqKv/3tb/Tq1Ysvv/ySs846izFjxvDRRx81SMFFREREQkFJSQnvvPMOa9eu5eGHH2bKlCmcddZZFBYWcthhhx20JbQ+Agp1N998MzfffDM5OTl89tlnfPrpp3z00Uc89dRT5Q/0JSUlcffdd3PKKadwyimnYLPZyvc/5ZRTmDp1Kvfeey+zZ8/mL3/5S4MUXkRERCSYmKbJxx9/zJtvvlmekWJiYvjhhx948skneeCBB+jQoQOzZs3i1Vdf5a233uLVV18F/COFOJ1O5syZg2maeL1eSkpKuOWWWwI6dp06SiQnJzNy5EhM0+Scc86hdevWvPnmm6SlpZGTk0NaWhqjR4+uFOjA/0zeHXfcwfz587nvvvu47LLLiI+Pr8uhRURERIJeUVERw4cPx+v1YhgGubm5TJw4kX/961/ceeedbNy4kcmTJ9OuXTuSk5NZvnw5n376aaXh3vav/DIMo2FD3b6238mTJzN+/Hiee+454uLi+OCDDxg0aBA9e/YkOzubRYsWsXr1at577z0uu+yy8jHrHn30Ub755hueeOIJRo0axaZNmzjmmGPqco1EREREgl58fDx33HEHxx9/PJdeeim33347X3/9NXv37uWtt94iIiKCk08+mXHjxvH444/Ttm1bZsyYAcDnn3/OwoULmTVrFuCvFCsqKgr42AGFuvT0dBwOB5MmTeL555+ndevWREb65zy76aabeOGFF3jqqaf49ddfmTZtGg8++CAPPvggl156Kffeey//+te/OO200zj77LP5/PPPFehEREQkbO3f8eHBBx/k/fff5+WXX2bIkCF06NCB4447jjPPPJO77rqLsrIyRo0ahcVioaioiPfff5/Ro0fX67gB9X7t2rUrX3zxBWvXruXWW28lIiICq9XKs88+ywsvvMBjjz3G3/72t/LtHQ4Hd9xxB59++inHHnsse/bs4frrrwdQoBMREZEWo2vXrsycOZM1a9bg8XhYunQpv/32G/feey+9e/fG5XKxatWqBjlWQKHum2++4YMPPiA7O5uuXbuSk5NDdnY2ffr04aGHHqJ///5888035dtHRUXx+OOP8/vvvzNw4EAAli5d2iAFFhEREQk1Rx55JCtWrKBLly4cccQRGIbB7bffTq9evSgrK2uQYwTU/PrYY4/xn//8p8rUFV9++SWmaWIYBueccw6nnHIKmZmZ5e+npKTg9Xrp1KkTTz/9NCeddBIjRoxokIKLiIiIBLv9O0B89tlnrFy5kvbt2zNv3jxKSkr45ZdfGuxYAYW6m266iUsvvRS73Y7dbucf//gHa9euJSUlhccee4z4+HhcLhfXXHMNpmkSFxfHmjVr6NGjBz///DNvv/02aWlp3HjjjQwbNqx8klsRERGRcLavo0NaWhpjx45l8ODBfPHFF2zatImnnnqKk08+mb59+zbIsQJqfj377LMZP348MTExdO3aldatW9OtWzfcbjfPPPMMZ5xxBpdddhkLFy5k8ODBFBYWMnDgQIYOHYppmpx22mk89dRT5OXl8eKLLzZIwXn7VXj24YrlXdvg0Zvg5pGw6GXYLxmLiIiINCWPxwNAQUEB33zzDRMmTCAlJaW8Z+v9999PSkoKI0aMIC8vr3yfffvVR0ChDijvzfryyy/j9Xrp06cPq1atIj09nfPOOw+fz8eIESM444wzSElJ4ZlnnmHXrl0cc8wxJCQk0L59e0aMGNEgc5uxaxt8sRQun+RfdrvguYeh8xHwwLOQvgNWfXLoxxERERGph5KSEgDy8/Pp2bMnRx55JPPnz6d3796Av//BnXfeyaZNmxg3bhwALpfrkJ6vCyjU7dixg+eff57777+fWbNm4Xa7KSsro2/fvrzzzjv8/PPP5QPluVwuvF4vN954I5s2beKf//xn+edceeWVbN++neXLl9e7wJgmzHsWzrwYWrf3r1v/A5QUw2XX+deNmABffVj/Y4iIiIgcgpiYGD7//HMGDx5McnIyq1ev5owzzqi0zbXXXss111xT3opZVlZ2SKEuoGfqOnXqxJo1a+jatSsARx99NBER/l1PO+00br31VtasWVOeMIuLiwGIjo7mpJNOKv+c008/nXHjxjF48OB6F5gvl8GfW+Dkc+Hnb6HvsfDnH9CtN0T+71m9w7r6a+tq4naBx12xXOqsf5lERERE9hMREcGpp55aaflA0dHRvPLKK+XL5557Lq1bt67/MQPdcF+gA6o0od5///14PB7sdjtPPfUUTz311EE/Iyoqijlz5tSzqPiD17tzoU0HyM2C1Z/Cf9Lg8D6Q2qZiO8MAixWKCyG2munIPlgAS96sf1lEREREGtCRRx7JkUceWe/96zT3a3UcDkdDfEzt1qyCslK4fTrExcOwy+Hh62HVxzD4rMrb2mzgKqs+1A0bDWfvN7xKbjY8dF3jlV1EREQkQCtXriQjI4Nhw4YRFxcX0D4Bd5Q4VMXFxaSnpx/ah+TuhW69/IEOwGr1N7W6XVCYX3nbUiccpKqznM0O0bEVr6joQyubiIiISDXeffddtm3bFvD2zz77LFdccQX5+fm1b/w/TRbqZsyYwaBBg/j222/r/yHJrfy1b/vL2QOXXQt//Faxbm+m/3m56mrpRERERJqI1+tl4sSJdO/enaFDh/LGG2/gdNb8LP++VtCkpKSAj9Mgza+1yc7O5qmnnqKgoIAffviBE044oX4fdNQJMH8WfPEfOPp4f3Psji3wl7vg/Tfg6+Uw6ExYthB6D/A/VyciItISTTw3sO1e0WgRjW379u106dKF//73v3z++ed88cUXTJ48mdGjRzN+/HgGDRpUZZ/o6OhK/xuIJqmpmzBhAvn5+Rx11FFcf/319f+guHj426PwzafwwERY/i5MuhfatIext8Abz8Gtl8OPX8GlExqs/CIiIiL11a1bN3744Qfy8vJYunQp1157LTExMbzyyiucfPLJ9OrVi6VLl1bax2r1V0wdOEVrTRq9pm769OksXbqUmJgY3nzzzfJC1tvhveHep6uuHzAIps2GbZuge2+IdxzacUREREQaUExMDMOGDWPYsGHMnDmTJUuW8Nxzz/H555/TqlWrQ/78Rg11Tz/9NPfeey9Wq5U333yzweY2q1ZSK/9LREREJIhZrVYuvvhiLr74Yn7++Wf69et3yJ/ZKKGupKSEW265hVdffRW73c7rr7/ORRdd1BiHEhEREQnY1nQXT8zLYVeWm2GD45h0iaPWJs77XtjD6vWl5cvH9IzkyVvaVLv9/Pnzefvtt0lMTCQuLq5OTaj7rFy5ss77NHioW7p0KbfddhubN2+mU6dOpKWlVZpVQkRERKQ5uNwm98/KYmDvaB74SwrPL8zlw9XFnHdSzePAbfrTzewH2pLq8Mem2p4k27p1K+++++4hl7euYbDBQt3nn3/O1KlTWbFiBYZhcP311/PYY4813cDEIiIiIjX47hcnxU6TG0Y6iLJbmHiRg2cW5NYY6rJyPZimSdf29oCPM2zYMA4//HCSkpKIjIysV03d888/zzvvvFOnfQIKdV9//TV2u53WrVsTExNDaWkpeXl57N69m++//560tDTWr1+P1Wrliiuu4L777qNPnz51PgERERGR+igp9VHs9JUv2yIM7LbKYWrLLje9u9qJsvsH/+jWwcb2DDc12bDNhc8Hl923i6ISHycdFc3frkgmPqb6AUQGDBjAgAEDDuFsqFdNX0Chbty4cfzxxx81bjN8+HBmzZpF+/bt61wIERERkUMxYWpmpeWxwxIYP9xRaV1JqY92KRXRxzAMLBYoLPFVG9J27nHTo5OdSSMcWAyDJ17P5pX38rj1iuRqy+L1eg99tI96CCjUtW/fnrZt2xITE4PP58Pj8bB792527dpFYWEh4H+W7ttvv2XChAlMnjyZww47rFELLiIiIrLPnAfb0spREWtsEVWbPK2WquvtEQalrupD3ZhzEhlzTsXydRc7eOSVvTWGuscff5yHH36YuLg4oqKisNlsdW6Crcv0YPsEFOpWrFhR7Xs7duzgq6++4osvvmDx4sVMnz6dZ555hptvvpmHH364TiMhi4iIiNRHTJSF2Oia51SIj7WyNd1VaV1JmQ+bNfDAFRdjIb/Ih8ttVmne3Z/P56OgoICCgoKAP/tAdQ2ChzyjRKdOnRgzZgwvvfQS6enpzJ49m9atW/PEE0/Qv39/1q5de6iHEBERETlkPTvb2bC1ItRlZntweyA+tvo49MjLWfy6tWLe+d+2u0hOsNQY6K666irWrFlDeno6+fn5uFwu3G53nV433XRTnc+vQacJs9lsTJgwgd9++4077riDLVu2cMopp7BkyZKGPIyIiIhInfXrHkmx08fHq4sASPuogGN6RmK1GBQ7fXi8ZpV9unWw88LbuWzYWsY365zMWZLHRafE13iczp07079/f9q2bUt8fDwRERFYrdaDvr777jtmzJhRZb3FUveI1ihzv0ZFRfHEE0+wZMkSTNNk5MiRVeY0ExEREWlKVqvBbWOSmZGWy6V372TF2hKuvdgBwMRpGaxe56yyz5hzEujc1sadz+1h5tu5XHhKPGPOSTikcvz22288+OCDHH744QwZMoR7772X9PT0Q/pMaORpws477zw++eQThg4dyujRo1m5ciXHHHNMYx5SREREpFpD+scwb4qdjdtd9O0WiSPe30s17dEOB90+wmpw59Up3Hl1yiEdNzc3lzfffJO5c+eyZs0aAEzTJCkpieHDh1NUVHRInw+NHOoATjrpJObMmcMVV1zBiBEj+Pnnn0lMTGzsw4qIiIgcVKojonx2iKbgcrno3r07eXl5mKZJYmIiI0aMYPTo0QwdOvSgw584nf5aw9LSUqKiogI6TqM0vx5o9OjRXHXVVfz555/cfPPNTXFIERERkaBgt9sZNWoUp512GmlpaWRmZjJ79mzOPvvsasez29drti69Z5sspj755JO899579OzZs6kOKSIiIhIUXnjhhTp1fjj++OOJjIys0zGaLNS1bt2aFStW0L9//6Y6pIiIiEhQqEug27BhA23atOG5554jLq76eWmrHKM+BasvBToRERFpafbu3YvH42H+/PkMGjSIkpKSGrd/9NFHufLKKznssMP44IMPAj5Ok4S6mTNnkpaW1hSHEhEREQkaixcv5sgjj+Tll1+moKCAb7/9loiImhtKJ0+ezEsvvURUVBTTp08P+FiNHuruvPNObrrpJu666y6Ki4sb+3AiIiIiza6wsJBx48YxcuRIfD4f0dHR2Gw2wN9xoiaDBg1i4sSJDBkyhOzs7ICP2WjP1JmmycSJE5kzZw79+vVjyZIlxMbGNtbhREREQsPEcwPb7pUPG7cc0qisVisrVqzg8ssv57nnniM5OZnZs2eXv79161beeecdoqKiiIqKqjLPa0lJCatWraJ79+4BH7NeoW7nzp1cf/31REZGEhUVhWmalJaWUlZWxn/+8x8Axo4dy5tvvsnQoUN55513iI6Ors+hREREREJOTEwM3333Ha1btz7o+xs3buTOO+/EMAxMs+r0ZODvXPHggw8GfMx6hbri4uKDPri3f8ocNWoUHo+H119/nYiICI477jjatm3LzJkz6dy5c30OKyIiIhL0rr76ajZs2IDD4SAmJobIyEisVivbtm0DYNKkSURERHDZZZfRq1cv+vbtS8+ePSuNWWcYBm3btiU5OTng4wYc6mbNmsXxxx/PscceW34wr9db/v7jjz/O/fffX7584YUXcuGFFwL+Ksbs7GzWrFnD2LFjWbFiRcAFFBEREQklGRkZ7N69m71792KxWPB6vXi93vKpwNLS0ipNC2YYBg6Hg9NPP52rrrqKiy++uF7HDSjUOZ1OHnroIXJzcxk1ahSXXnpplW0ObAveX9euXdm6dStPPfUU9913X70KKiIiIkFIzwhWsXz58oOuf+mll7jhhhvIzc3F4/GQlZXFzp07Wb9+PcuXL+fDDz9k8eLFdO/enWeeeYZzzw3w2v5PQL1fnU4nd999N4MGDWLhwoWMHj26TgfZZ/jw4Xg8nnrtKyIiIhJqiouLycjIAMDtdgNQVlZGZGQkhx12GCeeeCITJ07krbfeYufOnTz55JNkZWVx/vnn87e//a1Sq2htAgp1ycnJ3HHHHXz55Zds27aN2267LaAPX7RoEYMHDy6flFZERESkJXnxxRfp1q0bAH379uW2226rdr7XuLg4jjvuOFauXMmpp57Kc889x7Bhwyo11dakzh0lOnbsyMSJE3n66aer3cbpdHLDDTfw+uuvk5CQwLfffstpp51W10OJiIiIhLSYmBhiYmIAiIyMxOfz8fe//x2r1YrH46GkpIR//vOfZGdnc8stt5CWlsaVV17JJ598wuWXX86qVavIzc0NaLqwRhmnbvTo0SxdupSzzz6bV199lfbt2zfGYURERESCWkRERPkMEr/++iszZswgMjIS8I/p63a7ufHGGxkyZAh79uxhzJgxPPDAA1itVt544w2+//57OnbsGNixGuMEZs+ezVtvvcWNN95YYwcKERERkXBmsVgqNbe2bduW9PR0AH777Tf69u1L9+7dOe+88xgzZgxnnXVW+baRkZEMGTIk4GPVKdRlZGQwbtw4hg0bVuN2qamp3HTTTVXW//HHH3U5nIiIiEhIWrlyJbfeeitXXnlltdvsG3j49ttvJykpiWXLlrFs2bLy91q3bs3YsWNp165dQMcMONT99NNPnH/++WRkZARU+/bss88yZcqU8rnOvF4vWVlZOByOQA8pIiIiEnLefvttrr76auLi4tiwYUOt28+fP7/KutLSUgoKCvj3v//Nd999F9BxAwp1Xq+XUaNGkZ+fz8KFCznyyCPp27dvjfu0adOG/v37Y7fbiYyMJCIigujo6Fpr+URERERC2UsvvUS3bt34+OOP+fDDD1m6dGm12xqGUT7kyYFGjhzJ4sWLyc3NJSkpqdbjBhTqrFYrr7/+OlarlYEDB/L7779jmiZnnHFG+TY7duyotM/o0aPrPZ6diIiISKiaO3cudrudlJSUKu95PB7WrVuHaZps3bq1fP3LL79Meno69913HzabDYDOnTsTExNDRkZGw4U6gBNPPLH83zabjU6dOpGenl7eg8Nut9OpU6fybXbt2sWzzz7LRRddxKBBgwI9jIiIiEhI2/8ZOJ/PVz7oMMDevXvp378/4O/9uu+RtpkzZ/Lf//6Xt956ixkzZnDuuecybtw4pk+fXt57tjb16v3arVu38klpq/P666/zf//3fzz55JO0bt2akSNHMmbMGE466aT6HFJEREQk5LhcrvJJGE488URefPFFoqKiMAwDp9NZ/t7q1at5/vnneeyxxzj//PO57rrrmDVrVp2O1ShDmgDcc889jBgxgmXLlvHuu+/ywgsv8MILL/Dtt99y3HHHNdZhRURERIJGSkpKeWtn3759q+2TEBUVxR133MGVV17JNddcU+d5X6ERQx1Ajx496NGjB7fccgvbtm1j4cKFCnQiIiLSYlx++eVcfvnlAW/frl07Pvjgg3qN8xvQ3K8NoUuXLtx1111NdTgRERGRKramu7jh8UwuvP1PXnwnF9M0A97X4zX5y6MZ/LSxtBFLSL0nbmiyUCciIiLSnFxuk/tnZdGjk51Z97Rle4abD1cXB7z/Wx8XsDXdXfuGzUShTkRERFqE735xUuw0uWGkgw6pNiZe5GDZ14GFup173CxcXkDbFGvtGzcThToREREJeSWlPoqdFS+Xu2qz6pZdbnp3tRNl98efbh1sbM8IrObt6fk5XHFOIm2SG7U7wiEJ3pKJiIiIBGjC1MxKy2OHJTB+uKPSupJSH+1SKqKPYRhYLFBY4iM+pvp6rmXfFFHs9HHZ0Hi++8XZoOVuSAp1UncTA+xm/cqHjVsOERGR/5nzYFtaOSpijS2iamcDq6XqenuEQamr+lCXV+jllffymD65NVZr/TowNBWFOhEREQl5MVEWYqNrfqosPtbK1nRXpXUlZT5sNYS1mW/nMuykOLp3tDdIORuTQp2IiIi0CD072/lgVVH5cma2B7cH4mOrD4Offl9CTJTBu18WAuAsM7lvVhZXnZvAmHMSG73MdaFQJyIiIi1Cv+6RFDt9fLy6iLNPjCPtowKO6RmJ1WJQ7PQRaTeIOKDWbv7U9pWWp87ey6VnxHN8n+imLHpAFOpERESkRbBaDW4bk8y0Odn8a3EeXh/MuLU1ABOnZTB5ZBJD+sdU2qdtSuWoZLcZJCdYiauhY0VzUagTERGRFmNI/xjmTbGzcbuLvt0iccT7x51Le7RDQPvPuLVNYxbvkCjUiYiISIuS6ogg1RF+ESj46g5FREREpM4U6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImEgtEPdjPth1cf+f+/aBo/eBDePhEUvg2k2a9FEREREmlLohrrVn8EvP/r/7XbBcw9D5yPggWchfQes+qR5yyciIiLShEIz1BUVwsKXoe1h/uX1P0BJMVx2HbRuDyMmwFcfNm8ZRURERJpQaI68t/AlGDAI3GX+5T//gG69ITLKv3xYV39tXU3cLvC4K5ZLnY1TVhEREZEmEHqh7ref4befYMqLkDbLv85ZAqn7TdthGGCxQnEhxMYf/HM+WABL3mz04oqIiIg0hdAKdW4XvP4sXHUjRMdWrLdaAVvlbW02cJVVH+qGjYazR1Qs52bDQ9c1eJFFREREmkJohbol86FLDzj6hMrrY+P9vV/3V+qEiBpOz2b3v/ZxljRYMUVERESaWmiFuu8+h8J8uOlS/7KrDH5YCSltwOup2G5vpv95uepq6URERETCTGiFurueBJ+3YnnRK9CtFww6y990+vVyGHQmLFsIvQf4n6sTERERaQFCK9Qlp1ZejoyCuASIT4Sxt8DL0/1Bz/TBnU80TxlFREREmkFohboDXXNHxb8HDIJps2HbJujeG+IdzVYsERERkaYW2qHuQEmt/C8RERGRFiY0Z5QQERERkUoU6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAoU5EREQkDCjUiYiIiIQBhToRERGRWuzN87B+Sxklpb7mLkq1wmvwYREREZEabE138cS8HHZluRk2OI5JlzgwDKPGfRYuL+CNZfm0Topgd46HR69PpV+PqCYqceBUUyciIiItgsttcv+sLHp0sjPrnrZsz3Dz4eriGvf5c7ebBcsLmPNQe155oB2XnZnAnKX5TVTiulGoExERkRbhu1+cFDtNbhjpoEOqjYkXOVj2dc2hzuuD28ckk5JoBaDbYTaKSoKzCVbNryIiIhLySkp9FDsrwpYtwsBuq9ysumWXm95d7UTZ/XVa3TrY2J7hrvFzu7Sz0aWdDQBnqY93vyhiSP/oBi59w1CoExERkZA3YWpmpeWxwxIYP9xRaV1JqY92KRXRxzAMLBYoLPERH1Nz4+Xq9U6mzt5L25QIrjovscHK3ZAU6kRERCTkzXmwLa0cFbHGFlG184PVUnW9PcKg1FV7qBvYO4rHJ6fy3MJc/rU4j8kjkxqm4A1Iz9SJiIhIyIuJshAbXfE6sOkVID7WSl6Rt9K6kjIfNmvNvV8BrFaDo7pHceNlSXz4TVGDlbshKdSJiIhIi9Czs50NW13ly5nZHtweiI+tPg59+n0xC5cXlC9bLQZWS+0hsDko1ImIiEiL0K97JMVOHx+v9te0pX1UwDE9I7FaDIqdPjxes8o+HdvYmPuffFb+VEJmtofXluZz6oCYpi56QPRMnYiIiLQIVqvBbWOSmTYnm38tzsPrgxm3tgZg4rQMJo9MYkj/yoGtRyc7f7s8mVn/zqWoxMcpx8Rww0hHM5S+dgp1IiIi0mIM6R/DvCl2Nm530bdbJI54//hzaY92qHafs06I5awTYpuqiPWmUCciIiItSqojglRH+EUgPVMnIiIiEgYU6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAoU5EREQkDCjUiYiIiIQBhToRERGRMBB+I+8Fo4nnBr7tKx82XjlEREQkbKmmTkRERCQMKNSJiIiIhAGFOhEREZEwoFAnIiIiEgYU6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAoU5EREQkDCjUiYiIiIQBhToRERGRMKBQJyIiIhIGFOpEREREwoBCnYiIiEgYUKgTERERCQMRzV0AERERkaayNd3FE/Ny2JXlZtjgOCZd4sAwjBr3WfpVEa8tzSO/yEf/HlHcMy6FlERrE5U4cKqpExERkRbB5Ta5f1YWPTrZmXVPW7ZnuPlwdXGN+6zbXMqcJXncO74V86e2x+UxefGd3CYqcd0o1ImIiEiL8N0vToqdJjeMdNAh1cbEixws+7rmUPfnbg+3XJ7Msb2iSE2K4NyTYvl9u6uJSlw3an4VERGRkFdS6qPY6StftkUY2G2Vm1W37HLTu6udKLu/TqtbBxvbM9w1fu6wwXGVlv/c7aFDanDGp+AslYiIiEgdTJiaWWl57LAExg93VFpXUuqjXUpF9DEMA4sFCkt8xMfU3niZX+Rl6cpC7hvfqkHK3NAU6kRERCTkzXmwLa0cFbHGFlG184PVUnW9PcKg1BVYqHvmrVz6dovkxKOiD73AjUChTkREREJeTJSF2Oiag1l8rJWt6ZWfhysp82Gz1tz7FWDZ10X8d3MpL93X7pDK2ZjUUUJERERahJ6d7WzYWhHqMrM9uD0QH1tzHNqwrYyZb+fy4DWtSE4IvqFM9lGoExERkRahX/dIip0+Pl5dBEDaRwUc0zMSq8Wg2OnD4zWr7JNT4OWBWVlcflYCPTrZcZb6cJb6qmwXDEKv+XXtN7DgX5CzBzofARNuh/adYNc2mPMU7MmAk8+BkROhlsEERUREpOWwWg1uG5PMtDnZ/GtxHl4fzLi1NQATp2UweWQSQ/rHVNrnsx+KyS308eqSfF5dkl+x/oVOTVr2QIRWqNuT7g9uV98MPY6CtBdg7j/hjsfhuYeh77Fw3b2QNgtWfQJDzm7uEouIiEgQGdI/hnlT7Gzc7qJvt0gc8f7m1LRHOxx0+5FnJDDyjISmLGK9hVbza8afMGICDDwFEpPgtOGwfROs/wFKiuGy66B1e/82X33Y3KUVERGRIJTqiGBwv5jyQBcuQqumrt8JlZczd0LrdvDnH9CtN0RG+dcf1hXSd9T8WW4XePYbcLDU2bBlFREREWlCoRXq9udxw0dvw1mXQFYmpLapeM8wwGKF4kKIjT/4/h8sgCVvNk1ZRURERBpZ6Ia6xXMhKhpOGQbvzgVsld+32cBVVn2oGzYazh5RsZybDQ9d12jFFREREWlMoRnqflkDKz6A+2ZARIQ/uO3aVnmbUqf/verY7P7XPs6SRimqiIiISFMIrY4SAFkZ8Mp0uOpGaN/Zv65LD/jjt4pt9mb6m2erq6UTERERCTOhFepcZfDswzBgEPQ/yV8bV+qEI44EZzF8vdy/3bKF0HuA/7k6ERERkRYgtJpff/kRMnb4X18uq1j/+Gsw9hZ4eTosegVMH9z5RLMVU0RERKSphVaoGzAIXqlm/LlWbWHabNi2Cbr3hnhHkxZNREREpDmFVqirTVIr/0tERESkhQmtZ+pERERE5KAU6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAoU5EREQkDCjUiYiIiIQBhToRERGRMKBQJyIiIhIGFOpEREREwoBCnYiIiEgYUKgTERERCQMKdSIiIiJhQKFOREREJAwo1ImIiIiEAYU6ERERkTCgUCciIiItxtZ0Fzc8nsmFt//Ji+/kYppmQPvt2uPmojt2NnLpDo1CnYiIiLQILrfJ/bOy6NHJzqx72rI9w82Hq4tr3S9jr4d7X8iisMTXBKWsP4U6ERERaRG++8VJsdPkhpEOOqTamHiRg2Vf1x7q7nthD8MGxzVBCQ+NQp2IiIiEvJJSH8XOipfLXbVZdcsuN7272omy++NPtw42tme4a/3saX9tzWnHxDR4mRtaRHMXQERERORQTZiaWWl57LAExg93VFpXUuqjXUpF9DEMA4sFCkt8xMdUX8/VvlUEmdmeBi1vY1CoExERkZA358G2tHJUxBpbhFFlG6ul6np7hEGpq+ZQFyoU6kRERCTkxURZiI2uOZjFx1rZmu6qtK6kzIfNWjUAhqLQj6UiIiIiAejZ2c6GrRWhLjPbg9sD8bHhEYfC4yxEREREatGveyTFTh8fry4CIO2jAo7pGYnVYlDs9OHxBjZmXbBSqBMREZEWwWo1uG1MMjPScrn07p2sWFvCtRc7AJg4LYPV65zNW8BDpGfqREREpMUY0j+GeVPsbNzuom+3SBzxVgDSHu1Q435tUyL47IVOTVHEelOoExERkRYl1RFBqiP8IpCaX0VERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAoU5EREQkDCjUiYiIiIQBhToRERGRMKBQJyIiIhIGFOpEREREwoBCnYiIiEgYUKgTERERCQMKdSIiIiJhQKFOREREJAwo1ImIiIiEAYU6ERERkTCgUCciIiISBhTqRERERMKAQp2IiIhIGFCoExEREQkDCnUiIiIiYUChTkRERCQMKNSJiIiIhAGFOhEREZEwoFAnIiIiEgYimrsADWrXNpjzFOzJgJPPgZETwTCau1QiIiISJLamu3hiXg67stwMGxzHpEscGLVkhZ83ljIjLYe8Ih9XnpvAqKEJTVTaugmfmjq3C557GDofAQ88C+k7YNUnzV0qERERCRIut8n9s7Lo0cnOrHvasj3DzYeri2vcJ6/QywMvZnHGwFiev7MNy78rZu3vpU1U4roJn1C3/gcoKYbLroPW7WHEBPjqw+YulYiIiASJ735xUuw0uWGkgw6pNiZe5GDZ1zWHuuXfF5OcaOXq8xI4rLWNscMSWfZ1UROVuG7Cp/n1zz+gW2+IjPIvH9bVX1tXHbcLPO7yRV9JMUUuDwXp6VBS1rBlK6pDot+5M3yOcyjH0HF0nKY8TrD9t6Pj1O84ofBd03Ea3N58Lx5XEUUlHmKiKuqqbBEGdlvlZtUtu9z07monyu7frlsHG9sz3NRky043A3pElTfR9uoSySvv5TXsSTSQ8Al1zhJIbVOxbBhgsUJxIcTGV93+gwWw5M3yxfSiUjouWAGv92/8stZkQcfwOU44nYuOo+PoOMF/nHA6Fx2nzsaxngh7xd/7scMSGD/cUWmbklIf7VIqoo9hGFgsUFjiIz7m4I2XJaU+OreLLF+OiTLYm+dt2MI3kPAJdVYrYKu8zmYDV9nBQ92w0XD2iPLF9h4Pf969GVJSwRIerdKFhYX06dOHX3/9lfj4g1yDFkDXQNcAdA1a+vmDrgGE7zXw+Xxk5fro3q09EREVscYWUbXzg9VSdb09wqDUVX2os1oM7PvtY7cZlLrMBip9wwqfUBcb7+/9ur9SJ0RUc4o2u//1PxbgsAHHNlrxmkNBQQEAHTp0ICEhOHvqNDZdA10D0DVo6ecPugYQ3tegU6fAtouPtbI13VVpXUmZD5u1+t6v8bEW8ooqauacpeZBA2MwCI8qKYAuPeCP3yqW92b6n5k7WC2diIiItDg9O9vZsLUi1GVme3B7/MGtOr0O2GfzThetHNZGLWd9hU+o63EUOIvh6+X+5WULofcA/3N1IiIi0uL16x5JsdPHx6v9vVfTPirgmJ6RWC0GxU4fHm/VZtVBR0ezbksZa38vxes1Wbi8gON6RzV10QMSPs2vViuMvQVeng6LXgHTB3c+0dylalaRkZE8/PDDREZG1r5xmNI10DUAXYOWfv6gawC6BgBWq8FtY5KZNiebfy3Ow+uDGbe2BmDitAwmj0xiSP+YSvskxlm5YYSDu5/fQ2y0hSi7wR1XpTRH8WtlmKYZnE/71VfuXti2Cbr3hnhHc5dGREREgkxWnoeN21307RaJIz6wFr1de9xsz3TT74goYqODs6Ez/EKdiIiISAsUnFFTREREROpEoU5EREQkDCjUiYiIiIQBhbog9N5779GtWzciIiI44YQT2LBhAwDr169n4MCBJCUlceedd7L/45A1vXfTTTdhGEb5q3v37gGVY8WKFfTu3ZtWrVrx9NNPN+xJ1qIhr8Frr71W6fz3vV577bVay3HBBRdU2ufMM89slPM9UH3OHyA7O5uuXbuybdu2Sutr2686ofYdgOqvQXWfV5vm+g5Aw1+DlnIvgINfg1C8F0D9rkFN3/dQvB9IgEwJKps3bzaTkpLMBQsWmJmZmeaoUaPMQYMGmaWlpWaXLl3MSZMmmZs3bzaHDRtmvvrqq6ZpmjW+Z5qmedJJJ5n/+c9/zNzcXDM3N9csKCiotRx79uwxExISzClTppgbN240jznmGPOzzz5rtPPeX0Nfg7KysvJzz83NNf/880+zVatW5pYtW2otS7t27cx169aV71tUVNSo526a9Tt/0zTNrKws88QTTzQBc+vWreXra9uvOqH2HTDN6q9BdZ8XiOb4Dphmw18D02wZ9wLTrP4ahNq9wDTrdw1q+r6H4v1AAqdQF2SWLFlizpo1q3z5s88+M+12u7l48WIzKSnJLC4uNk3TNH/66Sdz8ODBpmmaNb7ndrvN+Ph4s7CwsE7lmDFjhtmzZ0/T5/OZpmma7777rnnllVce8vkFoqGvwYGmTZtmXnfddbWW488//zTbtm17qKdTZ/U5f9M0zaFDh5r//Oc/q/whq8u12V+ofQdMs/prUN3n1aa5vgOm2fDXoKXcC0yz+mtwoGC/F5hm/a5BTd/3ULwfSOAU6oLcrFmzzD59+piPPPKIed5555Wv9/l8ZlJSkmmaZo3v/fjjj2ZcXJx5+OGHm1FRUeY555xjbt++vdbjjh8/3rzhhhvKl9PT083evXs31GnVyaFeg/05nU6zdevWNd7o9/n3v/9tpqammh06dDBjYmLM0aNHmzk5OYd+QnUUyPmbplle23DgH7JAr82BQu07YJrVX4PqPq82wfIdMM1DvwYt5V5gmoF9D0LxXmCagV+Dg+1jmuFxP5Dq6Zm6IOZyuXjyySf561//SkFBAV27di1/zzAMrFYrubm5Nb63YcMG+vbtS1paGr/++is2m41JkybVeuwDPzMhIYFdu3Y17AkGoCGuwf7mz5/PiSeeSJcuXWo99saNGzn22GP56KOP+OGHH9i2bRv33Xdfg51bIAI9f4Bu3bod9DMCvTa17Rfs3wGo/hpU93m1CYbvADTMNWgp9wII7HsQavcCqNs1ONg+EPr3A6mZQl0Qe+CBB4iLi+O6664jIiKiytQuUVFRlJSU1PjelVdeyerVqxk4cCBdu3bl+eef5+OPP6agoKDGYx/4mfs+r6k1xDXY34svvsj1118f0LHvueceli1bRt++fenduzfTp0/n7bffPrQTqqNAz78mDbVfsH8H6vN5tQmG7wA0zDVoKfeCQIXavQDqdw0O/L6H+v1AahY+c7+GmU8++YQXX3yR1atXY7PZSE5OZv369ZW2KSwsxG631/jegRwOBz6fj4yMDBISEqo9fnJyMllZWbV+XmNq6GuwefNmNm/eXO9eaw6Hg71791JWVtYkcyfW5fxrcij7hdJ3oD6fV1dN/R2Ahr8G+4TrvSAQoXYvgPpdg4N930P5fiC1U01dEPrjjz+48sormTVrFn369AFg4MCBrF69unybbdu2UVZWRnJyco3v3XbbbSxcuLD8ve+//x6LxULHjh1rLMOBn/nTTz/RoUOHhjrFWjXkNdhn4cKFDB8+POA/5iNHjqz0md9//z1t27Ztkpt4Xc+/Jg21X7B/B+rzebVpzu8ANOw1aCn3gkCE0r0A6ncNqvu+h+r9QALU3A/1SWUlJSVm7969zWuvvdYsLCwsf7lcLjM1NdWcO3euaZqmOWnSJHP48OGmafp7tVX33ty5c83u3bubK1asMD/99FOzV69e5jXXXFN+vPz8fNPlclUpR1ZWlhkVFWV+9tlnptvtNs8//3zzxhtvbOzTN02z4a/BPieffPJBu+5Xdw2mTJlinnjiiebq1avNJUuWmG3btjX//ve/N/TpVlGf898fB+n1WNN+4fId2N+B16C6z9vXky/YvgM1lbm+16Cl3Av2d+A12CdU7gWmWb9rUNP3PRTvBxI4hbogs3jxYhOo8tq6dau5ePFiMzo62mzdurWZkpJirl+/vtJ+1b13zz33mA6Hw+zYsaN58803VxpfqXPnzubixYsPWpaZM2eaNpvNbNWqldm5c2czMzOz0c57f41xDUpKSky73W5u2LChyvGquwYul8u85pprzPj4ePPwww83p0yZYrrd7kY55/3V9/z3Odgfspr2C6fvwD4HXoOaPs80g+87UFuZ6/s9aCn3gn0Odg1C6V5gmvW7BrV930PtfiCBM0wzwKGkJSjs2rWLH374gUGDBpGamhrwe/W1efNmNmzYwKmnnlrjczdNqamvQbCp7znWd79Q+w60FE19DfQ9CE66H8j+FOpEREREwoA6SoiIiIiEAYU6ERERkTCgUCciIiISBhTqRERERMKAQp2IBL2MjAzy8/Mb9DNdLleDfp6ISHNTqBORoPfII4+QmprK1VdfTUN02P/vf/9Lly5dePbZZ2vcbtmyZTzwwAOHfDwRkaagUCciQS0jI4M33ngDt9vN2LFjMQzjkD/z7bffJiMjg+zs7Err7777bu69997yOS7nz5/PtGnT2LVr1yEfU0SksSnUiUhQe+CBBygpKWHMmDGcfvrpeDyeal8ul4uioiJycnLIzMw86OeZpsmCBQtwOBzcdtttlda/9tprPP/88+Vzep511lkAfPrpp41/oiIih0ihTkSC1pdffsmcOXMAf62ZzWar8RUZGUl8fDwpKSm0a9cOj8dT5TMXL17Mxo0buemmm0hMTCxf/+2337Jnzx6uuuqq8pHyhw8fTmRkJK+99lqTnK+IyKGIaO4CiIgcTF5eXvkzdA8++CCDBg2q9H5aWhrz5s1j+vTpHH300YC/ts3r9eJ2uykpKcFqtVb53Mcee4zo6GhuvvnmSuufe+45AG644YbydcnJyVx00UUsXLiQ9evXc+SRRzb0aYqINBiFOhEJSpMmTWLHjh0MHTqUKVOmVHmW7ocffgDg+OOP57TTTgvoM999911+/PFHDj/8cFq1alW+ftu2bSxcuJDzzjuvPCDuc+utt7Jw4UIee+wx5s+ff2gnJSLSiNT8KiJB56GHHmLhwoUkJyczd+7cBukc4XQ6uf322w/63p133onX6+XBBx+s8t6JJ57I8OHDSUtLY9myZYdcDhGRxqKaOhEJKvPmzWPq1KkYhsHcuXMpKirit99+q7Ld3r17AdixYwcbNmzA5/Ph8/lwuVy43W4GDhxYqfn1oYce4o8//sBiqfxb9o033uDtt99m/PjxnHTSSQct09NPP81nn33G9ddfz9q1a0lOTm7AMxYRaRiG2RCDPomINBCfz8d7773H1q1bue2224iMjKzXQMG5ubk4HA7AP97c+eefz+DBgykrKyMnJ4fNmzezZs0aTj/9dAoKCpg5cyZdunSpEvr2ee2111iwYAHHHHMMn376aflni4gEC9XUiUhQsVgsXHLJJeXLkZGRHHfcccydO7fSdi+88AIzZszgjTfe4LjjjiuvqSsrK6OsrIz4+Pjybd977z0cDgdvvPEGo0ePBvwDEJ911lkUFxczatQoJk+eXGO5jjjiCM4880yWL1/O448/zuOPP96AZy0icugU6kQkqNlsNqKjo+nevXul9fuaQDt06EDPnj1r/IyZM2cyefJkOnfuXL4uOjqa1q1bM3XqVC666CJuvfVWZs+ezezZs1m6dCkdOnQo33bw4MG0b9+eRYsW8fTTTzNlypQGPEMRkYahUCciQe1gw5LU5zOOOuqoSuuOOOIIvv/+e+Li4gB/OFy0aBEAJ510UqXn5rxeLwkJCTgcDv7+978fcnlERBqDQp2IBJXc3NxKAwrXp+eraZp4PB5KS0uJjo4mIuLgt7p9gW6fjIwMYmJiqnSEcLvdxMbG1rkcIiJNSaFORIJKamoqXq+30rpPP/202nB3+umn1/h5K1euZMiQIQEde926dfTq1avSOo/Hg8/nIyoqKqDPEBFpLgp1IhJUZs+ejc1mw263YxhGtc2vixYtYv78+UydOrXKTA8+nw+v10tpaSlHHHFEQMfNzMxkw4YN/PWvf6203ul0Av5n8EREgplCnYgElXHjxgW03fr16wEYMmRIwDNK1GT27Nn4fD4uvvjiSutLSkoA1PwqIkFPM0qISEiq7xCbHo+nyr6///4706dPp1+/fpxxxhmV3ts3yHFiYmL9Cioi0kQU6kQkJO0bkPjA5+8C2W//fX7//XfOOeccnE4nL7/8cqVn90zT5P333wegffv2DVBqEZHGo+ZXEQlJZWVlgL9nal24XK7yff/9738zYcIEioqKmDt3LgMHDgQgOzubLl264HQ68Xq9REVFcd555zXsCYiINDCFOhEJSfuC2b5n3gLldrspLS0FYODAgaSkpDBr1iyuvPLK8m1SUlIYOXIkW7Zs4dhjj+Xaa6+lXbt2DVd4EZFGoLlfRaRFKysrIzIysrmLISJyyBTqRERERMKAOkqIiIiIhAGFOhEREZEwoFAnIiIiEgYU6kRERETCgEKdiIiISBhQqBMREREJAwp1IiIiImFAoU5EREQkDPw/bzmljp4wPxMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个图形和两个y轴  \n",
    "fig, ax1 = plt.subplots()  \n",
    "ax2 = ax1.twinx()  \n",
    "\n",
    "#绘制图形   \n",
    "bar = ax1.bar(x, y1,  label='总人数', color='tomato', width=0.4)  \n",
    "line = ax2.plot(x, y2,label='就业率', color='royalblue', marker='o', ls='-.')  \n",
    "plt.yticks(np.arange(0,1,0.1))\n",
    "\n",
    "# 设置x轴和y轴的标签，指明坐标含义  \n",
    "ax1.set_xlabel('年份', fontdict={'size': 16})  \n",
    "ax1.set_ylabel('总人数',fontdict={'size': 16})  \n",
    "ax2.set_ylabel('就业率',fontdict={'size': 16})\n",
    "\n",
    "#添加图例  \n",
    "#lines= line+bar  \n",
    "#labels = [h.get_label(index=0) for h in lines] \n",
    "#labels='就业率'\n",
    "#plt.legend(line, labels, loc='upper right') \n",
    "\n",
    "#设置轴标签颜色  \n",
    "ax1.tick_params('y', colors='tomato')  \n",
    "ax2.tick_params('y', colors='royalblue')  \n",
    "#设置轴颜色  \n",
    "ax1.spines['left'].set_color('tomato')  \n",
    "ax2.spines['left'].set_color('tomato')  \n",
    "ax1.spines['right'].set_color('royalblue')  \n",
    "ax2.spines['right'].set_color('royalblue')  \n",
    "#去掉上轴线  \n",
    "ax1.spines['top'].set_visible(False)  \n",
    "ax2.spines['top'].set_visible(False)  \n",
    "\n",
    "#展示图片  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f62333d-f090-46fb-9bf0-cd462c915cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     0.9608\n",
      "1     0.9153\n",
      "2     0.9615\n",
      "3     0.9861\n",
      "4     0.9348\n",
      "5     0.9189\n",
      "6     0.9053\n",
      "7     0.8718\n",
      "8     0.7444\n",
      "9     0.7531\n",
      "10    0.8529\n",
      "11    0.8472\n",
      "12    0.8532\n",
      "13    0.8923\n",
      "14    0.9460\n",
      "15    0.9556\n",
      "16    0.9091\n",
      "17    1.0000\n",
      "Name: 就业率, dtype: float64\n",
      "count    18.000000\n",
      "mean      0.900461\n",
      "std       0.071064\n",
      "min       0.744400\n",
      "25%       0.857850\n",
      "50%       0.912200\n",
      "75%       0.953200\n",
      "max       1.000000\n",
      "Name: 就业率, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['就业率'])\n",
    "print(df['就业率'].describe())\n",
    "\n",
    "df1 = pd.DataFrame(df['就业率'].describe()) # 创建DataFrame\n",
    "df1.to_excel('text.xlsx',index=True) # 存表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9569048-8087-4604-a93d-11ebe2e8bfed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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